Qengineering / TensorFlow_Lite_Classification_RPi_64-bits

TensorFlow Lite classification on a bare Raspberry Pi 4 with 64-bit OS at 23 FPS

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TensorFlow_Lite_Classification_RPi_64-bits

TensorFlow Lite classification running on a bare Raspberry Pi 4

A fast C++ implementation of TensorFlow Lite classification on a bare Raspberry Pi 4. The Raspberry Pi 64 bits version of Ubuntu is used as operating system. Once overclocked to 1925 MHz, your app runs at 23 FPS without any hardware accelerator. Special made for a bare Raspberry Pi see: https://qengineering.eu/install-ubuntu-18.04-on-raspberry-pi-4.html

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Papers: https://arxiv.org/pdf/1712.05877.pdf
Training set: COCO with 1000 objects
Size: 224x224

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Benchmark.

Model	RPI 4 @1950 MHz - 64 OS
Mobile_V1 Lite	38.5 FPS
Mobile_V2 Lite	45.5 FPS
Inception_V2 Lite	10.0 FPS
Inception_V4 Lite	2.0 FPS

With a regular 32 bits Raspbian OS you get lower frame rates see: https://github.com/Qengineering/TensorFlow_Lite_Classification_RPi_32-bits

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Dependencies.

To run the application, you have to:

• A raspberry Pi 4 with a 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. Install 64-bit OS
  
• TensorFlow Lite framework installed. Install TensorFlow Lite
  
• OpenCV 64 bit installed. Install OpenCV 4.5
  
• Code::Blocks installed. ($ sudo apt-get install codeblocks)

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Installing the app.

To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/TensorFlow_Lite_Classification_RPi_64-bits/archive/refs/heads/master.zip
$ unzip -j master.zip
Remove master.zip and README.md as they are no longer needed.
$ rm master.zip
$ rm README.md

Your MyDir folder must now look like this:
tabby.jpeg
schoolbus.jpg
grace_hopper.bmp
Labels.txt
TensorFlow_Lite_Mobile.cpb
TensorFlow_Lite_Class.cpp

Next, choose your model from TensorFlow: https://www.tensorflow.org/lite/guide/hosted_models
Download a quantized model, extract the .tflite from the tarball and place it in your MyDir.

Now your MyDir folder may contain: mobilenet_v1_1.0_224_quant.tflite.
Or: inception_v4_299_quant.tflite. Or both of course.

Enter the .tflite file of your choice on line 54 in TensorFlow_Lite_Class.cpp
The image to be tested is given a line 84, also in TensorFlow_Lite_Class.cpp

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Running the app.

Run TestTensorFlow_Lite.cpb with Code::Blocks. More info or
if you want to connect a camera to the app, follow the instructions at Hands-On.

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